Developing a stochastic simulation model for the generation of residential water end-use demand time series
1. Developing a stochastic simulation model for the generation
of residential water end-use demand time series
A. Cominola1, M. Giuliani1, A. Castelletti1, A.M. Abdallah2, D.E. Rosenberg2
1 Dept. Electronics, Information, and Bioengineering - Hydroinformatics Lab, Politecnico di Milano
2 Dept. of Civil and Environmental Engineering, Utah State University
2. NRM
Urban population is growing
US
246.2
Urban population in millions
81%
Urban percentage
Mexico
84.392
77%
Colombia
34.3
73%
Brazil
162.6
85%
Argentina
35.6
90%
Ukraine
30.9
68%
Russia
103.6
73%
China
559.2Urban population in millions
42%Urban percentage
Turkey
51.1
68%
India
329.3
29%
Bangladesh
38.2
26%
Philippines
55.0
64%
Indonesia
114.1
50%
S Korea
39.0
81%
Japan
84.7
66%
Egypt
33.1
43%
S Africa
28.6
60%
Canada
26.3
Venezuela
26.0
Poland
23.9
Thailand
21.5
Australia
18.3
Netherlands
13.3
Peru
21.0
Saudi Arabia
20.9
Iraq
20.3
Vietnam
23.3
DR Congo
20.2
Algeria
22.0Morocco
19.4
Malaysia
18.1
Burma
16.5
Sudan
16.3
Chile
14.6
N Korea
14.1
Ethiopia
13.0
Uzbekistan
10.1
Tanzania
9.9
Romania
11.6
Ghana
11.3
Syria
10.2
Belgium
10.2
80%
94%
62%
33%
89%
81%
73%
81%
67%
27%
33%
65%
60%
69%
32%
43%
88%
62%
16%
37%
25%
54%
49%
51%
97%
Nigeria
68.6
50%
UK
54.0
90%
France
46.9
77%
Spain
33.6
77%
Italy
39.6
68%
Germany
62.0
75%
Iran
48.4
68%
Pakistan
59.3
36%
Cameroon
Angola
Ecuador
Ivory
Coast
Kazakh-
stan
Cuba
Afghan-
istan
Sweden
Kenya
Czech
Republic
9.5
9.3
8.7
8.6
8.6
8.5
7.8
7.6
7.6
7.4
Mozam-
bique
Hong
Kong
Belarus
Tunisia
Hungary
Greece
Israel
Guate-
mala
Portugal
Yemen
Dominican
Republic
Bolivia
Serbia &
Mont
Switzer-
land
Austria
Bulgaria
Mada-
gascar
Libya
Senegal
Jordan
Zimbabwe
Nepal
Denmark
Mali
Azerbaijan
Singapore
El
Salvador
Zambia
Uganda
Puerto
Rico
Paraguay
UAE
Benin
Norway
New
Zealand
Honduras
Haiti
Nicaragua
Guinea
Finland
Uruguay
Lebanon
Somalia
Sri Lanka
Cambodia
Slovakia
Costa Rica
Palestine
Kuwait
Togo
Chad
Burkina
Ireland
Croatia
Congo
Niger
Sierra Leone
Malawi
Panama
Turkmenistan
Georgia
Lithuania
Liberia
Moldova
Rwanda
Kyrgyzstan
Oman
Armenia
Bosnia
Tajikistan
CAR
Melanesia
Latvia
Mongolia
Albania
Jamaica
Macedonia
Mauritania Laos
Gabon
Botswana
Slovenia
Eritrea
Estonia
Gambia
Burundi
Papua New Guinea
Namibia
Mauritius
Guinea-Bissau
Lesotho E Timor
Bhutan
Swaziland
Trinidad & Tobago
The earth reaches a momentous
milestone: by next year, for the ļ¬rst time
in history, more than half its population
will be living in cities. Those 3.3 billion
people are expected to grow to 5 billion
by 2030 ā this unique map of the world
shows where those people live now
At the beginning of the 20th
century, the world's urban
population was only 220
million, mainly in the west
By 2030, the towns and
cities of the developing
world will make up 80%
of urban humanity
The new urban world
Urban growth, 2005ā2010
Predominantly urban
75% or over
Predominantly urban
50ā74%
Predominantly rural
25ā49% urban
Predominantly rural
0ā24% urban
Cities over 10 million people
(greater urban area)
Key
Tokyo
33.4
Osaka
16.6
Seoul
23.2
Manila
15.4
Jakarta
14.9
Dacca
13.8
Bombay
21.3
Delhi
21.1 Calcutta
15.5
Karachi
14.8
Shanghai
17.3
Canton
14.5
Beijing
12.7
Moscow
13.4
Tehran
12.1
Cairo
15.9
Istanbul
11.7
London
12.0
Lagos
10.0
Mexico
City
22.1
New York
21.8
Sao Paulo
20.4
LA
17.9
Rio de
Janeiro
12.2
Buenos
Aires
13.5
3,307,950,000The worldās urban population ā from a total of 6,615.9 million SOURCE: UNFPA GRAPHIC: PAUL SCRUTONAfrica Asia Oceania Europe
0.1%
Eastern Europe
-0.4%
Arab States
Latin America
& Caribbean North America
3.2%
2.4%
1.3%
2.8%
1.7%
1.3%
7. Smart meters deployment: OPPORTUNITIES
ā¢ End-use characterization
ā¢ Advanced management strategies
ā¢ Projections of water demand
8. Smart meters deployment: CHALLENGES
ā¢ Deployment and maintenance costs
ā¢ Big data collection, processing and analysis
ā¢ Intrusiveness and privacy
9. NRM
Generation of end-use water demand
5 Devices (indoor): toilet, clothes washer, showers, dishwasher, faucet
753,076 water-use events
over 4,036 monitoring days
across 9 US cities
Source: DeOreo [2011]
10. NRM
Generation of end-use water demand
DEVELOPMENT OF A SYNTHETIC WATER END-USE PATTERNS GENERATOR
An algorithm to generate synthetic water end-use patterns has been developed within the SmartH2O project, with the double pu
ā¢āÆ building end-use water consumption datasets to feed disaggregation algorithms and to provide benchmark datasets for com
testing;
ā¢āÆ allowing for the generation of end-use patterns under different demographic and technological scenarios.
DEVELOPMENT PLAN
ā¢āÆ User-friendly interface
ā¢āÆ Web portal to contribute with new
datasets from different case studies
CURRENT FEATURES
ā¢āÆ Trained on high-resolutions (1 second) consumption data from 9 cities across USA
ā¢āÆ Performance validated with a two-sample Kolmogorov-Smirnov test
ā¢āÆ Flexible for synthetic generation at multi-scale resolutions.
userās input end-use statistics extraction end-use traces generation
ā¢āÆ house size
ā¢āÆ time sampling
resolution
ā¢āÆ device
presence
usage duration
usage volume
time-of-use
frequency of use
typical pattern (signature)
34 %
Andrea Cominol
andrea.cominola@polimi
@smartH2Oproje
#SmartH2
time
water
consumption
5 Devices (indoor): toilet, clothes washer, showers, dishwasher, faucet
753,076 water-use events
over 4,036 monitoring days
across 9 US cities
Source: DeOreo [2011]
11. NRM
Generation of end-use water demand
DEVELOPMENT OF A SYNTHETIC WATER END-USE PATTERNS GENERATOR
An algorithm to generate synthetic water end-use patterns has been developed within the SmartH2O project, with the double pu
ā¢āÆ building end-use water consumption datasets to feed disaggregation algorithms and to provide benchmark datasets for com
testing;
ā¢āÆ allowing for the generation of end-use patterns under different demographic and technological scenarios.
DEVELOPMENT PLAN
ā¢āÆ User-friendly interface
ā¢āÆ Web portal to contribute with new
datasets from different case studies
CURRENT FEATURES
ā¢āÆ Trained on high-resolutions (1 second) consumption data from 9 cities across USA
ā¢āÆ Performance validated with a two-sample Kolmogorov-Smirnov test
ā¢āÆ Flexible for synthetic generation at multi-scale resolutions.
userās input end-use statistics extraction end-use traces generation
ā¢āÆ house size
ā¢āÆ time sampling
resolution
ā¢āÆ device
presence
usage duration
usage volume
time-of-use
frequency of use
typical pattern (signature)
34 %
Andrea Cominol
andrea.cominola@polimi
@smartH2Oproje
#SmartH2
time
water
consumption
5 Devices (indoor): toilet, clothes washer, showers, dishwasher, faucet
Duration
Volume
Time of use
# daily uses
12. NRM
Generation of end-use water demand
DEVELOPMENT OF A SYNTHETIC WATER END-USE PATTERNS GENERATOR
An algorithm to generate synthetic water end-use patterns has been developed within the SmartH2O project, with the double pu
ā¢āÆ building end-use water consumption datasets to feed disaggregation algorithms and to provide benchmark datasets for com
testing;
ā¢āÆ allowing for the generation of end-use patterns under different demographic and technological scenarios.
DEVELOPMENT PLAN
ā¢āÆ User-friendly interface
ā¢āÆ Web portal to contribute with new
datasets from different case studies
CURRENT FEATURES
ā¢āÆ Trained on high-resolutions (1 second) consumption data from 9 cities across USA
ā¢āÆ Performance validated with a two-sample Kolmogorov-Smirnov test
ā¢āÆ Flexible for synthetic generation at multi-scale resolutions.
userās input end-use statistics extraction end-use traces generation
ā¢āÆ house size
ā¢āÆ time sampling
resolution
ā¢āÆ device
presence
usage duration
usage volume
time-of-use
frequency of use
typical pattern (signature)
34 %
Andrea Cominol
andrea.cominola@polimi
@smartH2Oproje
#SmartH2
time
water
consumption
5 Devices (indoor): toilet, clothes washer, showers, dishwasher, faucet
Duration
Volume
Time of use
# daily uses
Typical pattern -
signature
13. NRM
Generation of end-use water demand
DEVELOPMENT OF A SYNTHETIC WATER END-USE PATTERNS GENERATOR
An algorithm to generate synthetic water end-use patterns has been developed within the SmartH2O project, with the double pu
ā¢āÆ building end-use water consumption datasets to feed disaggregation algorithms and to provide benchmark datasets for com
testing;
ā¢āÆ allowing for the generation of end-use patterns under different demographic and technological scenarios.
DEVELOPMENT PLAN
ā¢āÆ User-friendly interface
ā¢āÆ Web portal to contribute with new
datasets from different case studies
CURRENT FEATURES
ā¢āÆ Trained on high-resolutions (1 second) consumption data from 9 cities across USA
ā¢āÆ Performance validated with a two-sample Kolmogorov-Smirnov test
ā¢āÆ Flexible for synthetic generation at multi-scale resolutions.
userās input end-use statistics extraction end-use traces generation
ā¢āÆ house size
ā¢āÆ time sampling
resolution
ā¢āÆ device
presence
usage duration
usage volume
time-of-use
frequency of use
typical pattern (signature)
34 %
Andrea Cominol
andrea.cominola@polimi
@smartH2Oproje
#SmartH2
time
water
consumption
5 Devices (indoor): toilet, clothes washer, showers, dishwasher, faucet
14. NRM
Generation of end-use water demand
DEVELOPMENT OF A SYNTHETIC WATER END-USE PATTERNS GENERATOR
An algorithm to generate synthetic water end-use patterns has been developed within the SmartH2O project, with the double pu
ā¢āÆ building end-use water consumption datasets to feed disaggregation algorithms and to provide benchmark datasets for com
testing;
ā¢āÆ allowing for the generation of end-use patterns under different demographic and technological scenarios.
DEVELOPMENT PLAN
ā¢āÆ User-friendly interface
ā¢āÆ Web portal to contribute with new
datasets from different case studies
CURRENT FEATURES
ā¢āÆ Trained on high-resolutions (1 second) consumption data from 9 cities across USA
ā¢āÆ Performance validated with a two-sample Kolmogorov-Smirnov test
ā¢āÆ Flexible for synthetic generation at multi-scale resolutions.
userās input end-use statistics extraction end-use traces generation
ā¢āÆ house size
ā¢āÆ time sampling
resolution
ā¢āÆ device
presence
usage duration
usage volume
time-of-use
frequency of use
typical pattern (signature)
34 %
Andrea Cominol
andrea.cominola@polimi
@smartH2Oproje
#SmartH2
time
water
consumption
5 Devices (indoor): toilet, clothes washer, showers, dishwasher, faucet
15. NRM
Generation of end-use water demand
DEVELOPMENT OF A SYNTHETIC WATER END-USE PATTERNS GENERATOR
An algorithm to generate synthetic water end-use patterns has been developed within the SmartH2O project, with the double pu
ā¢āÆ building end-use water consumption datasets to feed disaggregation algorithms and to provide benchmark datasets for com
testing;
ā¢āÆ allowing for the generation of end-use patterns under different demographic and technological scenarios.
DEVELOPMENT PLAN
ā¢āÆ User-friendly interface
ā¢āÆ Web portal to contribute with new
datasets from different case studies
CURRENT FEATURES
ā¢āÆ Trained on high-resolutions (1 second) consumption data from 9 cities across USA
ā¢āÆ Performance validated with a two-sample Kolmogorov-Smirnov test
ā¢āÆ Flexible for synthetic generation at multi-scale resolutions.
userās input end-use statistics extraction end-use traces generation
ā¢āÆ house size
ā¢āÆ time sampling
resolution
ā¢āÆ device
presence
usage duration
usage volume
time-of-use
frequency of use
typical pattern (signature)
34 %
Andrea Cominol
andrea.cominola@polimi
@smartH2Oproje
#SmartH2
time
water
consumption
5 Devices: toilet, clothes washer, showers, dishwasher, faucet
21. NRM
Conclusions
This preliminary results show great potential for supporting smart meters
deployments and end-use water demand analysis.
Outlook:
1. Update of the database with new report by Aquacraft Inc.
2. Web service for community development
3. Creation of open database across countries